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Philadelphia chromosome-like (Ph-like) acute lymphoblastic leukaemia (ALL), a high-risk subtype characterised by genomic alterations that activate cytokine receptor and kinase signalling, is associated with inferior outcomes in most childhood ALL clinical trials. Half of the patients with Ph-like ALL have kinase rearrangements or fusions. We examined the frequency and spectrum of these fusions using a retrospective cohort of 212 newly diagnosed patients with childhood B-cell ALL. Samples without known chromosomal alterations were subject to multiplex reverse transcription polymerase chain reaction to identify known Ph-like kinase fusions. Immunoglobulin heavy chain locus (IGH) capture and kinase capture were applied to samples without known kinase fusions. We detected known kinase fusions in five of 212 patients, comprising EBF1-PDGFRB, ETV6-ABL1, ZC3HAV1-ABL2, EPOR-IGH, and CNTRL-ABL1. Two patients with P2RY8-CRLF2 were identified. Patients with non-Ph kinase fusions had inferior 5-year event-free survival and overall survival compared with patients with other common genetic alterations. The prevalence of non-Ph kinase fusions in our Taiwanese cohort was lower than that reported in Caucasian populations. Future clinical trials with tyrosine kinase inhibitors may be indicated in Taiwan because of the inferior outcomes for B-cell ALL with kinase fusions. [ABSTRACT FROM AUTHOR]

Although the cure rate for childhood acute lymphoblastic leukemia (ALL) has exceeded 80% with contemporary therapy, relapsed ALL remains a leading cause of cancer-related death in children. Relapse-specific mutations can be identified by comprehensive genome sequencing and might have clinical significance. Applying whole-exome sequencing to eight triplicate samples, we identified in one patient relapse-specific mutations in the folylpolyglutamate synthetase (FPGS) gene, whose product catalyzes the addition of multiple glutamate residues (polyglutamation) to methotrexate upon their entry into the cells. To determine the prevalence of mutations of the FPGS mutations, and those of two important genes in the thiopurine pathway, NT5C2 and PRPS1, we studied 299 diagnostic and 73 relapsed samples in 372 patients. Three more FPGS mutants were identified in two patients, NT5C2 mutations in six patients, and PRPS1 mutants in two patients. One patient had both NT5C2 and PRPS1 mutants. None of these alterations were detected at diagnosis with a sequencing depth of 1000X, suggesting that treatment pressure led to increased prevalence of mutations during therapy. Functional characterization of the FPGS mutants showed that they directly resulted in decreased enzymatic activity, leading to significant reduction in methotrexate polyglutamation, and therefore likely contributed to drug resistance and relapse in these cases. Thus, besides genomic alterations in thiopurine metabolizing enzymes, the relapse-specific mutations of FPGS represent another critical mechanism of acquired antimetabolite drug resistance in relapsed childhood ALL. [ABSTRACT FROM AUTHOR]

As the performance of current fall risk assessment tools is limited, clinicians face significant challenges in identifying patients at risk of falling. This study proposes an automatic fall risk prediction model based on eXtreme gradient boosting (XGB), using a data-driven approach to the standardized medical records. This study analyzed a cohort of 639 participants (297 fall patients and 342 controls) from Chang Gung Memorial Hospital, Chiayi Branch, Taiwan. A derivation cohort of 507 participants (257 fall patients and 250 controls) was collected for constructing the prediction model using the XGB algorithm. A comparative validation of XGB and the Morse Fall Scale (MFS) was conducted with a prospective cohort of 132 participants (40 fall patients and 92 controls). The areas under the curves (AUCs) of the receiver operating characteristic (ROC) curves were used to compare the prediction models. This machine learning method provided a higher sensitivity than the standard method for fall risk stratification. In addition, the most important predictors found (Department of Neuro-Rehabilitation, Department of Surgery, cardiovascular medication use, admission from the Emergency Department, and bed rest) provided new information on in-hospital fall event prediction and the identification of patients with a high fall risk. [ABSTRACT FROM AUTHOR]